# coding=utf-8 # Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Covid Dialog dataset in English and Chinese""" import copy import os import re import textwrap import datasets # BibTeX citation _CITATION = """\ @inproceedings{devaraj-etal-2021-paragraph, title = "Paragraph-level Simplification of Medical Texts", author = "Devaraj, Ashwin and Marshall, Iain and Wallace, Byron and Li, Junyi Jessy", booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics", month = jun, year = "2021", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/2021.naacl-main.395", pages = "4972--4984", """ # Official description of the dataset _DESCRIPTION = textwrap.dedent( """ "Paragraph-level Simplification of Medical Texts" (Devaraj et al.) studies the problem of learning to simplify medical texts. One of their contributions is a new corpus that is composed of technical abstracts and their lay summaries on various clinical topics. The author generated train/val/test splits, which are available in the GitHub repository linked in the paper. The following is an example from the dataset: { "doi": "10.1002/14651858.CD011112.pub2", "abstract": "We included six studies (reported as seven papers) involving 326 participants whose ages ranged from 39 to 83 years, with a gender bias towards men (73% to 95% across studies), reflecting the characteristics of patients with HNC. The risk of bias in the studies was generally high. We did not pool data from studies because of significant differences in the interventions and outcomes evaluated. We found a lack of standardisation and consistency in the outcomes measured and the endpoints at which they were evaluated. We found no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the safety and efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes. Using the GRADE system, we classified the overall quality of the evidence for each outcome as very low, due to the limited number of trials and their low quality. There were no adverse events reported that were directly attributable to the intervention (swallowing exercises). We found no evidence that undertaking therapeutic exercises before, during and/or immediately after HNC treatment leads to improvement in oral swallowing. This absence of evidence may be due to the small participant numbers in trials, resulting in insufficient power to detect any difference. Data from the identified trials could not be combined due to differences in the choice of primary outcomes and in the measurement tools used to assess them, and the differing baseline and endpoints across studies. Designing and implementing studies with stronger methodological rigour is essential. There needs to be agreement about the key primary outcomes, the choice of validated assessment tools to measure them and the time points at which those measurements are made.", "pls": "We included six studies with 326 participants who undertook therapeutic exercises before, during and/or after HNC treatment. We could not combine the results of the studies because of the variation in participants' cancers, their treatments, the outcomes measured and the tools used to assess them, as well as the differing time points for testing. Researchers have compared: (i) therapeutic exercises versus treatment as usual (TAU); (ii) therapeutic exercises versus sham therapy; (iii) therapeutic exercises plus TAU versus TAU. The therapeutic exercises varied in their design, timing and intensity. TAU involved managing patients' dysphagia when it occurred, including inserting a tube for non-oral feeding. The evidence is up to date to 1 July 2016. We found no evidence that therapeutic exercises were better than TAU, or any other treatment, in improving the safety and efficiency of oral swallowing (our primary outcome) or in improving any of the secondary outcomes. However, there is insufficient evidence to draw any clear conclusion about the effects of undertaking therapeutic exercises before during and/or immediately after HNC treatment on preventing or reducing dysphagia. Studies had small participant numbers, used complex interventions and varied in the choice of outcomes measured, making it difficult to draw reliable conclusions. There were no reported adverse events directly attributable to the intervention (swallowing exercises). The current quality of the evidence to support the use of therapeutic exercises before, during and/or immediately after HNC treatment to prevent/reduce dysphagia is very low. We need better designed, rigorous studies with larger participant numbers and agreed endpoints and outcome measurements in order to draw clear(er) conclusions." }, where "pls" stands for "plain-language summary". Paper: http://arxiv.org/abs/2104.05767 Code: https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts """ ) # Link to an official homepage for the dataset here _HOMEPAGE = "https://github.com/AshOlogn/Paragraph-level-Simplification-of-Medical-Texts" _LICENSE = "" import datasets import os import json class Builder(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [datasets.BuilderConfig(name="default", version=datasets.Version("1.0.0"), description=_DESCRIPTION)] def _info(self): features = datasets.Features( { "query": datasets.Value("string"), "answer": datasets.Value("string"), } ) return datasets.DatasetInfo( description=f"Covid Dialogue dataset, as preprocessed and shuffled in HELM", features=features, homepage=_HOMEPAGE, license=_LICENSE, citation=_CITATION, ) def _split_generators(self, dl_manager): test_target = dl_manager.download("test.source") test_source = dl_manager.download("test.source") train_source = dl_manager.download("train.source") train_target = dl_manager.download("train.target") val_source = dl_manager.download("valid.source") val_target = dl_manager.download("valid.target") return [ datasets.SplitGenerator( name=datasets.Split.TRAIN, gen_kwargs={"target": train_target, "source": train_source}, ), datasets.SplitGenerator( name=datasets.Split.VALIDATION, gen_kwargs={"target": val_target, "source": val_source}, ), datasets.SplitGenerator( name=datasets.Split.TEST, gen_kwargs={"target": test_target, "source": test_source}, ), ] # method parameters are unpacked from `gen_kwargs` as given in `_split_generators` def _generate_examples(self, source, target): with open(source, encoding="utf-8") as f_source: with open(target, encoding="utf-8") as f_target: for idx, (s, t) in enumerate(zip(f_source, f_target)): yield idx, {"query": s.rstrip(), "answer": t.rstrip()}